There are inherent issues with classifying imbalanced data, especially in classifying minority
class samples. With an emphasis on the use of deep generative methodologies, this study …

Abstract Machine learning (ML) based algorithms, due to their ability to model nonlinear and
complex relationship, have been used in predicting corrosion pit depth in oil and gas …

Many vital real-world applications involve time-series data with skewed distribution.
Compared to traditional imbalanced learning problems, the classification of imbalanced time …

As a system allowing intra-network devices to automatically communicate over the Internet,
the Internet of Things (IoT) faces increasing popularity in modern applications and security …

The increasing availability of condition assessment data highlights the challenge of
managing data imbalance in the asset management of aging infrastructure. Aging sewer …

Heavy-tailed distribution widely exists in economic, financial, industrial and other data. The
tail of heavy-tailed distribution is thicker than that of Gaussian distribution. Generative …

It is difficult to obtain a large amount of labeled data, which has become a bottleneck for the
application of deep learning to analyze one-dimensional optical time series signals. In order …

Imbalanced class is a common issue encountered in real-world datasets. Oversampling is a
technique used to tackle imbalanced classes, with the Synthetic Minority Oversampling …

This study introduces CCNETS (Causal Learning with Causal Cooperative Nets), a novel
generative model-based classifier designed to tackle the challenge of generating data for …

Various studies have shown the advantages of using Machine Learning (ML) techniques for
analog and digital IC design automation and optimization. Data scarcity is still an issue for …